Food Chemistry 170 (2015) 234–240

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Food Chemistry

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Differentiation of Anatolian samples from different botanical origins by ATR-FTIR spectroscopy using multivariate analysis ⇑ Seher Gok a, Mete Severcan b, Erik Goormaghtigh c, Irfan Kandemir d, Feride Severcan a, a Department of Biological Sciences, Middle East Technical University, 06531 Ankara, Turkey b Department of Electrical and Electronic Engineering, Middle East Technical University, 06531 Ankara, Turkey c Center for Structural Biology and Bioinformatics, Laboratory for the Structure and Function of Biological Membranes, Université Libre de Bruxelles, Brussels, Belgium d Department of Biology, Ankara University, Ankara, Turkey article info abstract

Article history: Botanical origin of the predominantly affects the chemical composition of honey. Analytical Received 27 March 2014 techniques used for reliable honey authentication are mostly time consuming and expensive. Addition- Received in revised form 8 August 2014 ally, they cannot provide 100% efficiency in accurate authentication. Therefore, alternatives for the Accepted 10 August 2014 determination of floral origin of honey need to be developed. This study aims to discriminate character- Available online 20 August 2014 istic Anatolian honey samples from different botanical origins based on the differences in their molecular content, rather than giving numerical information about the constituents of samples. Another scope of Keywords: the study is to differentiate inauthentic honey samples from the natural ones precisely. All samples were Honey tested via unsupervised pattern recognition procedures like hierarchical clustering and Principal Compo- Botanical origin ATR-FTIR spectroscopy nent Analysis (PCA). Discrimination of sample groups was achieved successfully with hierarchical cluster- À1 Multivariate analysis ing over the spectral range of 1800–750 cm which suggests a good predictive capability of Fourier Hierarchical Cluster Analysis Transform Infrared (FTIR) spectroscopy and chemometry for the determination of honey floral source. Principal Component Analysis Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Anklam, 2001). Authentication of honey has primary importance for both industries and consumers. Demand for ‘‘natural’’ honey Honey characteristics depend primarily on the botanical origin has been increasing especially in medical market due to its thera- of nectar. Floral source of the nectar predominantly affects the peutic effects. In addition, from the economic perspective, authen- chemical composition of honey in terms of its protein, carbohy- tication is needed to avoid unfair competition which can lead to a drate, enzyme, mineral and organic acid content. It is known that destabilization in market (Cordella, Moussa, Martel, Sbirrazzuoli, & there are more than 100 different botanical origins for the honey. Lizzani-Cuvelier, 2002). Turkey has suitable geographical and cli- According to the Codex Alimentarius Standard for Honey and the matic conditions for apiculture where approximately 6,600,000 European Union Council Directive related to honey; ‘‘The use of a hives resided and lead to a production of 94,694 tones of honey botanical designation of honey is allowed if it originates predomi- in the year 2013 (TUIK, 2013) and one of the most important honey nantly from the indicated floral source’’. Botanical denomination is producer and exporter in the worldwide. Anatolian honeys are rich used for the presentation of more than 50% of honey products. in pollen types per sample and 85% of the world’s floral types can Particularly, unifloral honey has a higher demand and commercial be found in Turkish honeys. Despite the great diversity of honeys value in the market. However, 60% of indications related to floral produced in Anatolia, there have been limited studies for the char- origin made by beekeepers are incorrect. Therefore reliable acterisation and classification by botanical or geographical origin. authentication by analytical techniques is important for certifica- Also these publications are limited to compositional analysis tion and quality control of honey (Bryant & Jones, 2001). In the (Kayacier & Karaman, 2008; Kucuk et al., 2007; Senyuva et al., European Union the composition, manufacture and marketing of 2009; Silici & Gokceoglu, 2007). With this study, for the first time honey is regulated by the Community Directive 74/409/EEC. As a we have classified the wide range of different authenticated floral community standard, information referring to honey’s geographi- types of honey from Anatolia using spectroscopic and chemometric cal and floral origin must be supplemented (Radovic, Goodacre, & methods. Many analytical techniques have been applied for reliable authenticity testing of honey like high performance liquid ⇑ Corresponding author. Tel.: +90 0312 210 5157; fax: +90 0312 210 7970. E-mail address: [email protected] (F. Severcan). chromatography (HPLC) (Swallow & Low, 1990), nuclear magnetic http://dx.doi.org/10.1016/j.foodchem.2014.08.040 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved. S. Gok et al. / Food Chemistry 170 (2015) 234–240 235 resonance (NMR) (Lindner, Bermann, & Gamarnik, 1996), gas chro- which have ‘‘organic’’ certificate. Taurus flower honey is collected matography (Low & South, 1995) and carbon isotope ratio analysis from the Taurus Mountains located at the south Mediterranean (White, Winters, Martin, & Rossmann, 1998). These techniques region of Anatolia. used for reliable honey authentication are mostly time consuming Rhododendron honey, locally called as ‘‘mad honey’’ or ‘‘toxic and expensive. Additionally, they cannot provide 100% success in honey’’, is made up of spring flowers of Rhododendron ponticum authentication. Infrared (IR) spectroscopy has been preferred as a (rhododendron plant). Rhododendrons mainly grow in the eastern rapid, non-destructive, reagent-free, operator independent and Black Sea Region of Turkey. Their phenolic content and antimicro- cheap technique in food industry for the quantification of various bial activities are quite different from the other honey plant food samples (Chalmers & Griffiths, 2002; Li-Chan, Chalmers, & species. Nectar contains andromedotoxin, which causes various Griffiths, 2010). IR spectroscopy was applied in different honey physiological effects in humans (Onat, Yegen, Lawrence, Oktay, & samples for the determination of botanical or geographical origin, Oktay, 1991). detection of adulteration and for the quantification of , glu- Chestnut honey is produced from both nectar and secretum cose, , , pH value and electrical conductivity (Chung, collection by honey bee. These are collected from various regions Ku, & Lee, 1999; Lichtenberg-Kraag, Hedtke, & Bienefeld, 2002; of Anatolia. Ruoff, 2006; Tewari & Irudayaraj, 2004). Chemometric methods honey is a kind of honeydew honey. It is produced via based on Fourier Transform Infrared (FTIR) spectroscopy were also using the secretum of an insect () living in used for honey adulteration (Rios-Corripio, Rojas-López, & the trunk of pine tree and collected by bees. Pine honey is a specific Delgado-Macuil, 2012; Subari, Saleh, Shakaff, & Zakaria, 2012) endemic product, and can be found only in Turkey and Greece. and characterisation with limited number of Mexican honeys Cedar honey, used in this study, was collected from the Taurus (Rios-Corripio, Rios-Leal, Rojas-López, & Delgado-Macuil, 2011). Mountains in the Mediterranean region of Turkey, and is mainly Etzold and Lichtenberg-Kraag (2008) have developed FTIR based originated from Cedar trees. PCA calibration models with German honeys. Although, there have Fake (adulterated) honey used in this study was collected from been many attempts for searching alternative methods for authen- Apis mellifera. Hives were fed with (sucrose) thus sugar tication of honey, some of these studies have been limited with was incorporated into the honey via bee-feeding. Study in this field certain number of unifloral honey sources and have not been has shown that adulteration is also possible via bee-feeding tested sufficiently with polyfloral samples (Ruoff, 2006) and with and this can cause chemical modifications of the honey quality Anatolian honeys specifically. similar to artificial adulteration via direct syrup incorporation to In the current study, it was aimed to estimate botanical origin of honey (Cordella, Militão, Clément, Drajnudel, & Cabrol-Bass, 2005). honey samples that are specific to Anatolia by applying two differ- produced from the xylem of maple tree and ent multivariate analysis techniques to the Attenuated Total contains primarily sucrose and water. Maple syrups retrieved to Reflectance (ATR)-FTIR spectroscopic data. With this work, it was the study are Canadian origin and were used as a non-honey con- intended to exemplify the usage and success of ATR-FTIR spectros- trol group. Fructose syrup was directly purchased from the market. copy coupled with multivariate analysis in botanical origin assign- Grape (pekmez) is traditional syrup produced by boiling ment with a high number of sample groups. This work will also of the pressed grape juice and special grape soil mixture or cream provide basis to honey adulteration determination studies. of lime. It is rich in both carbohydrates and minerals.

2. Experimental 2.2. Instrumentation and sample analysis 2.1. Samples Spectra from all samples were collected in the one-bounce ATR mode in a Spectrum 100 FTIR spectrometer (Perkin-Elmer Inc., A total of 144 honey samples were collected from different geo- Norwalk, CT, USA) equipped with a Universal ATR accessory. graphical regions of Turkey. The majority of samples used in this Samples were placed on Diamond/ZnSe crystal plate (Perkin- study were procured from well known certificated honey brands Elmer) and scanned from 4000 to 650 cmÀ1 for 50 scans with which have BRC (British Retail Consortium) certificate and resolution of 4 cmÀ1 at room temperature. Each sample was repli- officially declare that honeys are subjected to all chemical and cated three times. Identical spectra were obtained in each case. This physical analysis to detect quality and purity in addition to process was done to see the accuracy of the absorbance values, descriptive organoleptic and microscopic analysis to determine which might be affected from intra-sample variability and from floral and regional origins. Some samples were collected directly variation in experimental conditions. Average spectra were used from the primary producers. The region and origin of production for further analysis. Data manipulations were carried out via were known for all samples. Spectrum 100 software (Perkin-Elmer). Flower originated (polyfloral (n = 30), anzer (n = 3), organic (n = 13), Taurus flower (n = 6)), tree originated (pine (n = 22), chestnut (n = 10), cedar (n = 6)) and rhododendron honey (n = 30) 2.3. Chemometrics samples in addition to fake (adulterated) honey (n = 6), maple syrup (n = 6), fructose syrup (n = 6) and grape molasses (n =6) Cluster and Principal Component Analysis were applied to clas- samples were included in the study. The sample size of each group sify the samples based on spectral differences. For the determina- is indicated as ‘‘n’’. tion of spectral differentiation among studied groups, cluster Honey samples were grouped as tree and flower originated ones analysis was performed via OPUS 5.5 software (Bruker Optics, basically. Flower originated group is composed of polyfloral hon- GmbH). Vector normalised, first derivative of each spectrum in eys, collected from different regions of Turkey, which are Anzer the range of 1800–750 cmÀ1 was used as an input data. Spectral honey, organic flower honey and Taurus flower honey. Anzer distances were calculated between pairs of spectra as Pearson’s honey, composed of nectar, mainly collected from Anzer plant correlation coefficients and Euclidean distance was used to calcu- (thymus species) in the narrow region located in Rize/Ikizdere/ late the sample similarities and to indicate the complete linkage Anzer. It has been largely studied in terms of its medicinal proper- clustering by Ward’s algorithm. ties that cause its market price to be 10 times higher than other Principle Component Analyses (PCA) was used as a data reduc- honeys. Organic honeys are collected from the well known brands tion method where each spectrum, which consists of hundreds of 236 S. Gok et al. / Food Chemistry 170 (2015) 234–240 absorbance values, is represented by a point in a multidimensional classification of different honey samples has been proposed in pre- space using a linear transformation. vious researches. In 1960, discriminant functions of monosaccha- In this work, PCA was conducted on the ATR-FTIR spectra ride and ash content in addition to pH values were used for over 4000–650 cmÀ1 1700–1600 cmÀ1, 1175–940 cmÀ1 and 940– classification of honey samples (Kirkwood, Mitchell, & Smith, 700 cmÀ1 range using by ‘‘Kinetics’’, a custom made program 1960). Linear discriminant analysis was employed to select most running under MATLAB (Matlab, Mathworks Inc.). useful measurands by evaluating different , water, pH value, colour, diastase enzyme activity conductivity and hyroxymethyl- 3. Results & discussion furfural content. Later, by using pH value, free acidity, electrical conductivity, fructose, and raffinose contents, botanical In the current study, ATR-FTIR spectroscopy has been used to origins of honeys were estimated perfectly (Devillers, Morlot, compare honey samples based on their spectral differences in the Pham-Delegue, & Dore, 2004). In addition, flower honey was char- 4000–650 cmÀ1 spectral region. A representative ATR-FTIR spec- acterised by high concentration values for glucose and fructose and trum of honey is given in Fig. 1. Table 1 presents the band assign- low free acidity, polyphenol content, lactone quantity and electri- ments along with the corresponding modes of vibrations in the cal conductivity; whereas honeydew honeys have low concentra- ATR-FTIR spectrum of honey, based on the literature (Gallardo- tion glucose and fructose while showing high free acidity, Velázquez, Osorio-Revilla, Loa, & Rivera-Espinoza, 2009; Kelly, polyphenol content, lactone quantity and electrical conductivity Downey, & Fouratier, 2004; Movasaghi, Rehman, & Rehman, (Sanz, Gonzalez, de Lorenzo, Sanz, & Martinez-Castro, 2005). 2008; Sivakesava & Irudayaraj, 2001; Subari et al., 2012; Tewari The algorithms behind the cluster and Principal Component & Irudayaraj, 2004, 2005). Analysis that were used in the current study are quite different. Fig. 2 shows comparative infrared spectra of all samples in the PCA-like techniques can be preferred primarily for the determina- 4000–650 cmÀ1 region. In this figure, spectral differences between tion of general relationship among data (Gasper et al., 2010). How- the groups were clearly seen. ever, if one wants to show the grouping of similar data gathered Based on the spectral differences Hierarchical Cluster Analysis from different samples, cluster analysis must be performed (HCA) and PCA have been applied to different spectral regions. (Wang & Mizaikoff, 2008). Similar samples tend to be classified Use of chemometrics together with classical methods for the in the same cluster and the level of difference between the clusters

Fig. 1. Representative ATR-FTIR spectrum of honey in the 4000–650 cmÀ1 spectral region.

Table 1 General band assignment of ATR-FTIR spectrum of honey. The related references are indicated in the parenthesis.

Region 1 3000–2800 cmÀ1 C–H stretching (carbohydrates) (Gallardo-Velázquez et al., 2009) O–H stretching (carboxylic acids) (Movasaghi et al., 2008)

NH3 stretching (free amino acids) (Gallardo-Velázquez et al., 2009; Sivakesava & Irudayaraj, 2001) Region 2 1700–1600 cmÀ1 O–H stretching/bending (water) (Cai & Singh, 2004; Stuart, 1997) C@O stretching (mainly from carbohydrates) (Gallardo-Velázquez et al., 2009) N–H bending of amide I (mainly proteins) (Philip, 2009) Region 3 1540–1175 cmÀ1 O–H stretching/bending (Gallardo-Velázquez et al., 2009; Tewari & Irudayaraj, 2004) C–O stretching (carbohydrates) (Tewari & Irudayaraj, 2004) C–H stretching (carbohydrates) (Tewari & Irudayaraj, 2005) C@O stretching of ketones (Tewari & Irudayaraj, 2004) Region 4 1175–940 cmÀ1 C–O & C–C stretching (carbohydrates) (Subari et al., 2012; Tewari & Irudayaraj, 2005) Ring vibrations (mainly from carbohydrates) (Gallardo-Velázquez et al., 2009; Tewari & Irudayaraj, 2004) Region 5 940–700 cmÀ1 Anomeric region of carbohydrates (Mathlouthi & Koenig, 1986; Subari et al., 2012) C–H bending (mainly from carbohydrates) (Gallardo-Velázquez et al., 2009; Kelly et al., 2004; Tewari & Irudayaraj, 2004) Ring vibrations (mainly from carbohydrates) (Tewari & Irudayaraj, 2004) S. Gok et al. / Food Chemistry 170 (2015) 234–240 237

Fig. 2. Comparative ATR-FTR spectra of all samples in the 4000–650 cmÀ1 spectral region. Spectra were normalised to the band located at 3300 cmÀ1.

Fig. 3. Hierarchical clustering of all samples in the 1800–750 cmÀ1 (fingerprint) spectral region.

is indicated with heterogeneity values (Ward, 1963). Ward’s algo- that only two groups, which show the smallest growth in heteroge- rithm was previously reported to give one of the best predictions, neity factor H, are merged. Detailed information about this method among the different methods used in cluster analysis (Lasch, was reported in Severcan et al. (2010). Haensch, Naumann, & Diem, 2004; Severcan, Bozkurt, Gurbanov, In this study, in order to reduce the number of variables prior to & Gorgulu, 2010). As opposed to other methods, algorithm tries performing cluster analysis, we used the PCA. This was conducted to find groups which are as homogeneous as possible. This means on four different regions. Depending on the PCA outputs, the 238 S. Gok et al. / Food Chemistry 170 (2015) 234–240

Fig. 4. PCA scatter plots for all of the samples over 4000–650 cmÀ1 (A), 1700–1600 cmÀ1 (B), 1175–940 cmÀ1 (C), and 940–700 cmÀ1 (D) spectral region. (Ellipses have a confidence factor of 0.8.) regions having the highest principal component (PC) values were 1800–750 cmÀ1 spectral region was selected for successful selected for HCA. Other spectral regions, used for PCA analysis, discrimination of clusters. were also tried for hierarchical clustering of the sample groups. For the calculation of sample similarities, the Euclidean distance However, the best differentiation was achieved only in the was used indicating the complete linkage clustering values. The 1800–750 cmÀ1 region. This region includes the anomeric region results obtained are represented in Fig. 3 in the form of dendro- at 950–750 cmÀ1 which was frequently preferred for the spectral grams. Clear cut classes were gathered over the range of 1800– analysis of carbohydrates in IR spectroscopy. Analysis in this range 750 cmÀ1 with high heterogeneity values (up to 10). All of the tree makes it possible to distinguish bands characteristic for a and b originated samples (chestnut, cedar, pine) are aggregated in one conformers or pyranoid and furanoid ring vibrations of mono cluster on the left arm. As the maple syrup is also the maple tree and polysaccharides (Mathlouthi & Koenig, 1986). In addition to originated sample, it shows more similarity to tree originated alpha and beta conformers, the fingerprint region (1800– group than to the flower originated ones. One arm of the second 750 cmÀ1) contains other contributions that arise from different cluster is composed of flower originated honey samples including molecules. Especially water (around 1640 cmÀ1) and minute polyfloral, anzer, organic, Taurus flower and rhododendron honeys. amount of protein molecules give bands in the indicated region. Anzer, organic and Taurus flower honeys are region specific sam- Also the differences among honey samples can be related not only ples, it is known that the purity of their botanical origins is higher to different water content in the different honeys but also to the than polyfloral honey group. So they were clustered in the same interaction between water molecules and carbohydrates, depend- arm. As the rhododendron honey is collected from the Black Sea ing on their structure. The precise assignment of bands in this Region mountains, it was clustered closer to that group than region cannot be stated unequivocally. However fingerprint region polyfloral ones. Fructose syrup, grape molasses and the fake provides a unique spectrum for each compound where the (adulterated) honey were agglomerated on the far right arm of position and intensity of the bands are specific for every the second cluster in that they differ from the natural samples in polysaccharide (Filippov, 1992; Li-Chan et al., 2010). Therefore, terms of their carbohydrate content significantly. S. Gok et al. / Food Chemistry 170 (2015) 234–240 239

Based on spectral differences a mean-centered PCA was conducted to all samples on the infrared spectra over the range of 4000–650 cmÀ1 (Fig. 4A), 1700–1600 cmÀ1 (Fig. 4B), 1175– 940 cmÀ1 (Fig. 4C) and 940–700 cmÀ1 (Fig. 4D). Distinct segregation and clustering between the samples were apparent in all figures. Samples were grouped close together creating uniform clusters for each of the analysed honey types in the PCA scatter plot. PCA is a data reduction method where each spectrum, which consists of hundreds of absorbance values, is represented by a point in a multidimensional space using a linear transformation. The coordinates of the point are the principal components (PC) and the plot obtained is called the scores plot. The transformation matrix in PCA consists of the eigenvectors of the covariance matrix of the whole set of spectra. Thus, each spectrum can be represented as the sum of a number of weighted orthonormal eigenvectors, where the weights are the PCs. In PCA the eigenvectors are ordered such that corresponding eigenvalues appear in decreasing order. Since the eigenvalues represent the variance of the corresponding eigenvectors, the first eigenvector describes the highest part of var- iability; the second eigenvector describes the second largest source of variability, and so on. Therefore, the corresponding scores defin- ing a specific spectrum indicate the amount of contribution of each eigenvector to this spectrum. An important property of PCA is that Fig. 5. The first 4 eigenvectors (PCA loading spectra) for the spectral region 1175– no other orthogonal transformation can give smaller error (in 940 cmÀ1. mean squared sense) than that of PCA if the number of transform coefficients is truncated at some value. In practice, the first 3 or weights of proteins in honey can vary depending on the honeybee 4 PCs are sufficient to represent an FTIR spectrum and observe species (Won, Lee, Ko, Kim, & Rhee, 2008). Immunological charac- the significant differences among spectra. Therefore, scores of sim- terisation of honey major protein and its application have been ilar spectra in a multidimensional score plot are clustered. On the reported by Won, Li, Kim, and Rhee (2009). The bands in the region other hand, as the dissimilarity of clusters increases the clusters 1700–1600 cmÀ1 had been previously assigned as amide I vibra- are separated from each other. tions of the honey proteins (Philip, 2009). However, water mole- PCA was applied to FTIR spectra of all groups, obtaining an evi- cules give strong absorption between 1640 and 1650 cmÀ1 (Cai & dent discrimination (score plot) of the different pure honey sam- Singh, 2004) so the discrimination in this region can be explained ples with respect to the fake honey, grape molasses, fructose and by the difference in water content, protein content and water– maple syrups (Fig. 4A). In practice, it is more convenient to plot carbohydrate interactions between sample groups. 2-D plots of any two of the PCs. Such a plot is the projection of a PCA results of the regions lying between the 1175–940 cmÀ1 multidimensional plot onto a 2-D space. Therefore it may be possi- and 940–700 cmÀ1 are given in Fig. 4C and D, respectively. Here ble to have overlaps of clusters in one plot and one has to check highly successful discrimination of all samples is achieved due to other combinations of PCs in other plots to observe cluster separa- differences in their carbohydrate content and structure. In tions. Here, successful significant differentiation of all investigated addition, tree originated samples were separated from flower groups has been achieved in a single 2D plot. Only rhododendron originated samples clearly, in both figures. samples make penetrations to both tree and flower originated The peaks observed in the loading spectra explain the chemical groups. This result is in consistence with the fact that rhododen- basis of the discrimination between different types of honey dron can be classified as brier, therefore, located closer to both tree (Fig. 5). As mentioned above, PCA provides a decomposition of an and flower originated samples. Infrared modes of water are very FTIR spectrum X in terms of a set of eigenvectors (or PCA loading intense and may overlap with the carbohydrate modes. The major P vectors/spectra) V ,asinX = x V , where x are the score values. infrared bands of water located at 3920 cmÀ1, 3490 cmÀ1 and i i i i i This implies that for positive score values of x positive peaks, 3280 cmÀ1 for O–H stretching and 1645 cmÀ1 for H–O–H bending i and for negative score values of x negative peaks of the PCA load- vibrations (Stuart, 1997). In the scope of this study, primary goal i ing spectra V have significant contribution to the spectrum X. For is the discrimination of honey samples coming from different floral i example, using the PCA results for the interval 1175–940 cmÀ1 origins rather than giving numerical information about the constit- and the corresponding eigenvectors shown in Fig. 5, it can be seen uents of samples. Also as it can be seen in the general honey spec- that chestnut honey is characterised by a high score value on PC1 trum (Fig. 1), vibrations coming from bulk water at 2250 cmÀ1 are (which explains 79.7% of the total variance). PC1 has a strong con- negligible. So the water in the samples is mainly bounded water. tribution from the line at around 1020 cmÀ1 (carbohydrate band). Water content itself can be used as a parameter for honey charac- This contribution is particularly high in chestnut honey. However, terisation (Manikis & Thrasyvoulou, 2001; Persano Oddo & Piro, using only two PCs in general cannot provide a complete represen- 2004). Thus, vibrations coming from water may also contribute tation and contributions of other loading spectra may need to be the discrimination success in 4000–650 cmÀ1 region, in addition taken into account to see how significant the observed contribu- the differences in carbohydrate content and structure of samples. tion of the peak is. A clear splitting of the data can be observed as depicted in Fig. 4B, by the first two principal components in the 1700– 1600 cmÀ1 region. This describes, 97.1% of the total variance for 4. Conclusion PC1 and 2.4% for PC2. Studies revealed that pollen proteins can be used as a marker for botanical classification of honey. At least The results from the current study point out that there are nineteen different protein bands were visualised by SDS–PAGE many considerable variations in the spectral parameters of honey experiments in honeys of different botanical origin; molecular samples which come from different botanical origins. Especially 240 S. 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